



'''simple docstring'''



import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def        UpperCAmelCase (    A	:					ndarray					):
							return np.dot(A       ,		A					)
class 						lowerCamelCase_				:







						def __init__(	self :   int					,     *,
						    lowerCAmelCase__ :   float = np.inf					,     lowerCAmelCase__ :   str = "linear"					,     lowerCAmelCase__ :   float = 0.0					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      regularization
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      gamma
													if kernel == "linear":
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      self.__linear
													elif kernel == "rbf":
																				if self.gamma == 0:
																											raise ValueError('''rbf kernel requires gamma'''					)
																				if not isinstance(self.gamma					,     (float, int)					):
																											raise ValueError('''gamma must be float or int'''					)
																				if not self.gamma > 0:
																											raise ValueError('''gamma must be > 0'''					)
																				SCREAMING_SNAKE_CASE       :   Any         =      self.__rbf
																				# in the future, there could be a default value like in sklearn
																				# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
																				# previously it was 1/(n_features)
													else:
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      F"""Unknown kernel: {kernel}"""
																				raise ValueError(lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   ndarray					,     lowerCAmelCase__ :   ndarray					):

													"""simple docstring"""
													return np.dot(lowerCAmelCase__					,     lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   ndarray					,     lowerCAmelCase__ :   ndarray					):

													"""simple docstring"""
													return np.exp(-(self.gamma * norm_squared(vectora - vectora					))					)







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   list[ndarray]					,     lowerCAmelCase__ :   ndarray					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      observations
													SCREAMING_SNAKE_CASE       :   int         =      classes

													# using Wolfe's Dual to calculate w.
													# Primal problem: minimize 1/2*norm_squared(w)
													#   constraint: yn(w . xn + b) >= 1
													#
													# With l a vector
													# Dual problem: maximize sum_n(ln) -
													#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
													#   constraint: self.C >= ln >= 0
													#           and sum_n(ln*yn) = 0
													# Then we get w using w = sum_n(ln*yn*xn)
													# At the end we can get b ~= mean(yn - w . xn)
													#
													# Since we use kernels, we only need l_star to calculate b
													# and to classify observations

													((SCREAMING_SNAKE_CASE)       ,)       :   Tuple         =      np.shape(lowerCAmelCase__					)

													def to_minimize(lowerCAmelCase__ :   ndarray					) -> float:
																				SCREAMING_SNAKE_CASE       :   str         =      0
																				((SCREAMING_SNAKE_CASE)       ,)       :   List[str]         =      np.shape(lowerCAmelCase__					)
																				for i in range(lowerCAmelCase__					):
																											for j in range(lowerCAmelCase__					):
																																		s += (
																																		    candidate[i]
																																		    * candidate[j]
																																		    * classes[i]
																																		    * classes[j]
																																		    * self.kernel(observations[i]					,     observations[j]					)
																																		)
																				return 1 / 2 * s - sum(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      LinearConstraint(lowerCAmelCase__					,     0					,     0					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      Bounds(0					,     self.regularization					)

													SCREAMING_SNAKE_CASE       :   List[Any]         =      minimize(
													    lowerCAmelCase__					,     np.ones(lowerCAmelCase__					)					,     bounds=lowerCAmelCase__					,     constraints=[ly_contraint]					).x
													SCREAMING_SNAKE_CASE       :   Tuple         =      l_star

													# calculating mean offset of separation plane to points
													SCREAMING_SNAKE_CASE       :   Dict         =      0
													for i in range(lowerCAmelCase__					):
																				for j in range(lowerCAmelCase__					):
																											s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																											    observations[i]					,     observations[j]					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      s / n







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   ndarray					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      sum(
													    self.optimum[n]
													    * self.classes[n]
													    * self.kernel(self.observations[n]					,     lowerCAmelCase__					)
													    for n in range(len(self.classes					)					)					)
													return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()









'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							List[str]             =    {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase_  :							Any             =    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]
def        UpperCAmelCase (    A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {}
							with open(A       ,		'''r'''					) as file:
														for line_number, line in enumerate(A					):
																					SCREAMING_SNAKE_CASE       :   Dict         =      line.strip()
																					if line:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      line.split()
																												SCREAMING_SNAKE_CASE       :   Any         =      line_number
																												SCREAMING_SNAKE_CASE       :   Any         =      words[0]
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							return result
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					List[Any]       ,		A	:					str       ,		A	:					int					):
							for attribute in key.split('''.'''					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      getattr(A       ,		A					)

							SCREAMING_SNAKE_CASE       :   Any         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   List[Any]         =      getattr(A       ,		A					).shape
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   List[str]         =      hf_pointer
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   int         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   int         =      shape_pointer.shape

														# let's reduce dimension
														SCREAMING_SNAKE_CASE       :   Dict         =      value[0]
							else:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
														    F""" {value.shape} for {full_name}"""					)

							if weight_type == "weight":
														SCREAMING_SNAKE_CASE       :   str         =      value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE       :   List[str]         =      value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE       :   Dict         =      value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							else:
														SCREAMING_SNAKE_CASE       :   Tuple         =      value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""					)
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[str]       ,		A	:					Optional[int]       ,		A	:					Tuple       ,		A	:					Tuple					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   str         =      '''.'''.join([key, weight_type]					)
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''.'''.join([key, hf_param_name]					)
							else:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      key

							SCREAMING_SNAKE_CASE       :   int         =      value if '''lm_head''' in full_key else value[0]


lowerCAmelCase_  :							Union[str, Any]             =    {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}
def        UpperCAmelCase (    A	:					List[Any]       ,		A	:					Tuple       ,		A	:					Dict=None       ,		A	:					Optional[int]=None					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      False
							for key, mapped_key in MAPPING.items():
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
														if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      True
																					if "*" in mapped_key:
																												SCREAMING_SNAKE_CASE       :   int         =      name.split(A					)[0].split('''.'''					)[-2]
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      mapped_key.replace('''*'''       ,		A					)
																					if "weight_g" in name:
																												SCREAMING_SNAKE_CASE       :   List[Any]         =      '''weight_g'''
																					elif "weight_v" in name:
																												SCREAMING_SNAKE_CASE       :   int         =      '''weight_v'''
																					elif "bias" in name:
																												SCREAMING_SNAKE_CASE       :   str         =      '''bias'''
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''weight'''
																					else:
																												SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
																					if hf_dict is not None:
																												rename_dict(A       ,		A       ,		A       ,		A       ,		A					)
																					else:
																												set_recursively(A       ,		A       ,		A       ,		A       ,		A					)
																					return is_used
							return is_used
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					Any       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      []
							SCREAMING_SNAKE_CASE       :   Any         =      fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE       :   List[str]         =      hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE       :   str         =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A       ,		A       ,		A       ,		A       ,		hf_model.config.feat_extract_norm == '''group'''       ,		)
																					SCREAMING_SNAKE_CASE       :   List[str]         =      True
														else:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_wavaveca_layer(A       ,		A       ,		A					)
														if not is_used:
																					unused_weights.append(A					)

							logger.warning(F"""Unused weights: {unused_weights}"""					)
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					int       ,		A	:					Tuple       ,		A	:					int       ,		A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   str         =      full_name.split('''conv_layers.'''					)[-1]
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      name.split('''.'''					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      int(items[0]					)
							SCREAMING_SNAKE_CASE       :   int         =      int(items[1]					)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   str         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
							else:
														unused_weights.append(A					)
@torch.no_grad()
def        UpperCAmelCase (    A	:					Any       ,		A	:					Union[str, Any]       ,		A	:					Optional[Any]=None       ,		A	:					Any=None       ,		A	:					Any=True       ,		A	:					str=False					):
							if config_path is not None:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      WavaVecaConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaConfig()

							if is_seq_class:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      read_txt_into_dict(A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      idalabel
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaForSequenceClassification(A					)
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaFeatureExtractor(
														    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
														feature_extractor.save_pretrained(A					)

							elif is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      Dictionary.load(A					)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE       :   List[str]         =      target_dict.pad_index
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.bos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      target_dict.eos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      len(target_dict.symbols					)
																					SCREAMING_SNAKE_CASE       :   str         =      os.path.join(A       ,		'''vocab.json'''					)
																					if not os.path.isdir(A					):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A					)					)
																												return
																					os.makedirs(A       ,		exist_ok=A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					SCREAMING_SNAKE_CASE       :   str         =      0
																					SCREAMING_SNAKE_CASE       :   Tuple         =      1
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as vocab_handle:
																												json.dump(A       ,		A					)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaCTCTokenizer(
																					    A       ,		unk_token=target_dict.unk_word       ,		pad_token=target_dict.pad_word       ,		bos_token=target_dict.bos_word       ,		eos_token=target_dict.eos_word       ,		word_delimiter_token='''|'''       ,		do_lower_case=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      True if config.feat_extract_norm == '''layer''' else False
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaFeatureExtractor(
																					    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaProcessor(feature_extractor=A       ,		tokenizer=A					)
																					processor.save_pretrained(A					)

														SCREAMING_SNAKE_CASE       :   List[str]         =      WavaVecaForCTC(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaForPreTraining(A					)

							if is_finetuned or is_seq_class:
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
							else:
														SCREAMING_SNAKE_CASE       :   str         =      argparse.Namespace(task='''audio_pretraining'''					)
														SCREAMING_SNAKE_CASE       :   Dict         =      fairseq.tasks.setup_task(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,		task=A					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model[0].eval()

							recursively_load_weights(A       ,		A       ,		not is_finetuned					)

							hf_wavavec.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							List[Any]             =    argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
							parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							parser.add_argument(
							    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
							)
							parser.add_argument(
							    '--is_seq_class',
							    action='store_true',
							    help='Whether the model to convert is a fine-tuned sequence classification model or not',
							)
							lowerCAmelCase_  :							Union[str, Any]             =    parser.parse_args()

							lowerCAmelCase_  :							Dict             =    not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)




